Date of Award

Summer 2026

Document Type

Open Access Thesis

Department

Computer Science and Engineering

First Advisor

Homayoun Valafar

Abstract

Brain-computer interfaces provide people who cannot feasibly control a keyboard and mouse with an alternative method to type, to move a cursor, or to generally issue commands and make selections. A non-invasive recording modality for brain-computer interfaces is electroencephalography, or EEG. The OpenBCI Ultracortex Mark IV is an EEG headset enabling relatively low-cost EEG acquisition using wet or dry electrodes. P300 is an event-related potential involving a deflection in an EEG channel's voltage typically 300 milliseconds after the onset of an "oddball" stimulus. A person's P300 response can be used to control a selector application. This study investigates classification of the visual P300 using the Mark IV, an example selector application, and finally a novel approach to flashing screen options (random subsets) and determining a user's desired option, demonstrated using the macOS Accessibility Keyboard. A neural network trained on data from a visual P300 task using a 10% probability oddball and evaluated on a separate test session achieved a .87 weighted F1 score, demonstrating cross-session generalization. The same model achieved a .90 weighted F1 score on data from a selector P300 task flashing one of 10 options with uniform probability, demonstrating cross-task generalization. Another neural network pretrained using the previous model, fine-tuned on data from the selector task, and evaluated on a live deployment session achieved a .87 weighted F1 score, but only typing accuracy of 41.7% at 5.19 characters per minute, demonstrating the effect of false positives. Another neural network trained on data from another selector task based on the macOS Accessibility Keyboard and evaluated within-subject achieved a .89 weighted F1 score on a participant wearing dry electrodes, with a simulated deployment performance of 62.32 seconds per key with 95.69% typed accuracy using 78 keys. This work contributes to the field of brain-computer interaction and could be applied to empower people with muscular or spinal disabilities to directly control any program on their computer.

Rights

© 2026, Grant King

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